Methods and apparatus to correct misattributions of media impressions

ABSTRACT

An example apparatus includes an impression collector to receive, at a first internet domain, a first request from a client device, the first request indicative of access to media at the client device; and send, from the first internet domain, a response to the client device, the response to instruct the client device to send a second request to a second internet domain; an audience adjustment factor determiner to determine an audience adjustment factor for a demographic group based on first impressions reported by the client device to the first internet domain and second impressions reported by the client device to the second internet domain; and a unique audience corrector to determine a misattribution-corrected unique audience size for the demographic group based on the audience adjustment factor and based on a second unique audience size determined at the second internet domain for the demographic group.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/146,912, filed on Sep. 28, 2018, which is a continuation of U.S.patent application Ser. No. 14/528,495, filed Oct. 30, 2014, whichclaims the benefit of, and priority from, U.S. Provisional PatentApplication Ser. No. 61/923,959 filed on Jan. 6, 2014, all of which arehereby incorporated herein by reference in their entireties.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to monitoring media and, moreparticularly, to methods and apparatus to correct misattributions ofmedia impressions.

BACKGROUND

Traditionally, audience measurement entities determine audienceengagement levels for media based on registered panel members. That is,an audience measurement entity enrolls people who consent to beingmonitored into a panel. The audience measurement entity then monitorsthose panel members to determine media (e.g., television programs orradio programs, movies, DVDs, advertisements, streaming media, websites,etc.) exposed to those panel members. In this manner, the audiencemeasurement entity can determine exposure metrics for different mediabased on the collected media measurement data.

Techniques for monitoring user access to Internet resources such as webpages, advertisements and/or other Internet-accessible media haveevolved significantly over the years. Some known systems perform suchmonitoring primarily through server logs. In particular, entitiesserving media on the Internet can use known techniques to log the numberof requests received for their media (e.g., content and/oradvertisements) at their server.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example client device that reports audienceimpressions for media to impression collection entities to facilitateidentifying total impressions and sizes of unique audiences exposed todifferent media.

FIG. 2 is an example communication flow diagram of an example manner inwhich an audience measurement entity (AME) and a database proprietor(DP) can collect impressions and demographic information based on aclient device reporting impressions to the AME and the DP.

FIG. 3 illustrates example impressions collected by the AME and exampleimpressions collected by the DP with a misattribution error.

FIG. 4 illustrates example audience adjustment (AA) factors for uniqueaudience sizes of different demographic groups determined based on theexample impressions of FIG. 3.

FIG. 5 illustrates example impression adjustment (IA) factors for totalimpressions of different demographic groups determined based on theexample impressions of FIG. 3.

FIG. 6 illustrates example misattribution-corrected unique audiencevalues and example misattribution-corrected impression counts determinedbased on the example AA factors of FIG. 4 and the example IA factors ofFIG. 5 for different demographic groups.

FIG. 7 illustrates example misattribution-corrected unique audiencevalues and example misattribution-corrected impression counts determinedbased on the example IA factors of FIG. 5 and example impressionfrequencies for different demographic groups.

FIG. 8 is a flow diagram representative of example machine readableinstructions that may be executed to implement the misattributioncorrector of FIG. 2 to determine misattribution-corrected uniqueaudience sizes and misattribution-corrected impression counts.

FIG. 9 illustrates an example processor system structured to execute theexample instructions of FIG. 8 to implement the example AME of FIGS. 1and/or 2.

DETAILED DESCRIPTION

Techniques for monitoring user access to Internet-accessible media suchas web pages, advertisements, content and/or other media have evolvedsignificantly over the years. At one point in the past, such monitoringwas done primarily through server logs. In particular, entities servingmedia on the Internet would log the number of requests received fortheir media at their server. Basing Internet usage research on serverlogs is problematic for several reasons. For example, server logs can betampered with either directly or via zombie programs which repeatedlyrequest media from the server to increase the server log counts.Secondly, media is sometimes retrieved once, cached locally and thenrepeatedly viewed from the local cache without involving the server inthe repeat viewings. Server logs cannot track these repeat views ofcached media. Thus, server logs are susceptible to both over-countingand under-counting errors.

The inventions disclosed in Blumenau, U.S. Pat. No. 6,108,637,fundamentally changed the way Internet monitoring is performed andovercame the limitations of the server side log monitoring techniquesdescribed above. For example, Blumenau disclosed a technique whereinInternet media to be tracked is tagged with beacon instructions. Inparticular, monitoring instructions are associated with the hypertextmarkup language (HTML) of the media to be tracked. When a clientrequests the media, both the media and the beacon instructions aredownloaded to the client. The beacon instructions are, thus, executedwhenever the media is accessed, be it from a server or from a cache.

The beacon instructions cause monitoring data reflecting informationabout the access to the media to be sent from the client that downloadedthe media to a monitoring entity. Typically, the monitoring entity is anaudience measurement entity (AME) that did not provide the media to theclient and who is a trusted (e.g., neutral) third party for providingaccurate usage statistics (e.g., The Nielsen Company, LLC).Advantageously, because the beaconing instructions are associated withthe media and executed by the client browser whenever the media isaccessed, the monitoring information is provided to the AME irrespectiveof whether the client is a panelist of the AME.

Audience measurement entities and/or other businesses often desire tolink demographics to the monitoring information. To address this issue,the AME establishes a panel of users who have agreed to provide theirdemographic information and to have their Internet browsing activitiesmonitored. When an individual joins the panel, they provide detailedinformation concerning their identity and demographics (e.g., gender,age, ethnicity, income, home location, occupation, etc.) to the AME. Theaudience measurement entity sets a cookie on the panelist computer thatenables the audience measurement entity to identify the panelistwhenever the panelist accesses tagged media and, thus, sends monitoringinformation to the audience measurement entity.

Since most of the clients providing monitoring information from thetagged media are not panelists and, thus, are unknown to the audiencemeasurement entity, it is necessary to use statistical methods to imputedemographic information based on the data collected for panelists to thelarger population of users providing data for the tagged media. However,panel sizes of audience measurement entities remain small compared tothe general population of users. Thus, a problem is presented as to howto increase panel sizes while ensuring the demographics data of thepanel is accurate.

There are many database proprietors operating on the Internet. Thesedatabase proprietors provide services to large numbers of subscribers.In exchange for the provision of the service, the subscribers registerwith the proprietor. As part of this registration, the subscribersprovide detailed demographic information. Examples of such databaseproprietors include social network providers, email providers, etc. suchas Facebook, Myspace, Twitter, Yahoo!, Google, etc. These databaseproprietors set cookies or other device/user identifiers on the clientdevices of their subscribers to enable the database proprietor torecognize the user when they visit their website.

The protocols of the Internet make cookies inaccessible outside of thedomain (e.g., Internet domain, domain name, etc.) on which they wereset. Thus, a cookie set, for example, in the amazon.com domain isaccessible to servers in the amazon.com domain, but not to serversoutside that domain. Therefore, although an audience measurement entitymight find it advantageous to access the cookies set by the databaseproprietors, they are unable to do so.

The inventions disclosed in Mainak et al., U.S. Pat. No. 8,370,489,which is incorporated by reference herein in its entirety, enable anaudience measurement entity to leverage the existing databases ofdatabase proprietors to collect more extensive Internet usage anddemographic data by extending the beaconing process to encompasspartnered database proprietors and by using such partners as interimdata collectors. The inventions disclosed in Mainak et al. accomplishthis task by structuring the AME to respond to beacon requests fromclients (who may not be a member of an audience member panel and, thus,may be unknown to the audience member entity) and redirect the clientfrom the audience measurement entity to a database proprietor such as asocial network site partnered with the audience member entity. Theredirection initiates a communication session between the clientaccessing the tagged media and the database proprietor. The databaseproprietor (e.g., Facebook) can access any cookie it has set on theclient to thereby identify the client based on the internal records ofthe database proprietor. In the event the client corresponds to asubscriber of the database proprietor, the database proprietor logs animpression in association with the demographics data associated with theclient and subsequently forwards logged impressions to the audiencemeasurement company. In the event the client does not correspond to asubscriber of the database proprietor, the database proprietor mayredirect the client to the audience measurement entity and/or anotherdatabase proprietor. The audience measurement entity may respond to theredirection from the first database proprietor by redirecting the clientto a second, different database proprietor that is partnered with theaudience measurement entity. That second database proprietor may thenattempt to identify the client as explained above. This process ofredirecting the client from database proprietor to database proprietorcan be performed any number of times until the client is identified andthe media exposure logged, or until all database proprietor partnershave been contacted without a successful identification of the client.The redirections all occur automatically so the user of the client isnot involved in the various communication sessions and may not even knowthey are occurring.

Periodically or aperiodically the partnered database proprietors providetheir logs and demographic information to the audience measuremententity which then compiles the collected data into statistical reportsaccurately identifying the demographics of persons accessing the taggedmedia. Because the identification of clients is done with reference toenormous databases of users far beyond the quantity of persons presentin a conventional audience measurement panel, the data developed fromthis process is extremely accurate, reliable and detailed.

Significantly, because the audience measurement entity remains the firstleg of the data collection process (e.g., receives the request generatedby the beacon instructions from the client), the audience measuremententity is able to obscure the source of the media access being logged aswell as the identity of the media itself from the database proprietors(thereby protecting the privacy of the media sources), withoutcompromising the ability of the database proprietors to log impressionsfor their subscribers. Further, when cookies are used as device/useridentifiers, the Internet security cookie protocols are complied withbecause the only servers that access a given cookie are associated withthe Internet domain (e.g., Facebook.com) that set that cookie.

Examples disclosed in Mainak et al. (U.S. Pat. No. 8,370,489) can beused to determine any type of media impressions or exposures (e.g.,content impressions, advertisement impressions, content exposure, and/oradvertisement exposure) using demographic information, which isdistributed across different databases (e.g., different website owners,service providers, etc.) on the Internet. Not only do such disclosedexamples enable more accurate correlation of Internet advertisementexposure to demographics, but they also effectively extend panel sizesand compositions beyond persons participating in the panel of anaudience measurement entity and/or a ratings entity to personsregistered in other Internet databases such as the databases of socialmedia sites such as Facebook, Twitter, Google, etc. Such extensioneffectively leverages the media tagging capabilities of the ratingsentity and the use of databases of non-ratings entities such as socialmedia and other websites to create an enormous, demographically accuratepanel that results in accurate, reliable measurements of exposures toInternet media such as advertising and/or programming.

In illustrated examples disclosed herein, media exposure is measured interms of online Gross Rating Points. A Gross Rating Point (GRP) is aunit of measurement of audience size that has traditionally been used inthe television ratings context. It is used to measure exposure to one ormore media (e.g., programs, advertisements, etc.) without regard tomultiple exposures of the same media to individuals. In terms oftelevision (TV) advertisements, one GRP is equal to 1% of TV households.While GRPs have traditionally been used as a measure of televisionviewership, examples disclosed herein may be used in connection withgenerating online GRPs for online media to provide a standardized metricthat can be used across the Internet to accurately reflect onlineadvertisement exposure. Such standardized online GRP measurements canprovide greater certainty to advertisers that their online advertisementmoney is well spent. It can also facilitate cross-medium comparisonssuch as viewership of TV advertisements and online advertisements,exposure to radio advertisements and online media, etc. Because examplesdisclosed herein may be used to correct impressions that associateexposure measurements with corresponding demographics of users, theinformation processed using examples disclosed herein may also be usedby advertisers to more accurately identify markets reached by theiradvertisements and/or to target particular markets with futureadvertisements.

Traditionally, audience measurement entities (also referred to herein as“ratings entities”) determine demographic reach for advertising andmedia programming based on registered panel members. That is, anaudience measurement entity enrolls people that consent to beingmonitored into a panel. During enrollment, the audience measuremententity receives demographic information from the enrolling people sothat subsequent correlations may be made between advertisement/mediaexposures to those panelists and different demographic markets. Unliketraditional techniques in which audience measurement entities relysolely on their own panel member data to collect demographics-basedaudience measurements, example methods, apparatus, and/or articles ofmanufacture disclosed herein enable an audience measurement entity toshare demographic information with other entities that operate based onuser registration models. As used herein, a user registration model is amodel in which users subscribe to services of those entities by creatingan account and providing demographic-related information aboutthemselves. Sharing of demographic information associated withregistered users of database proprietors enables an audience measuremententity to extend or supplement their panel data with substantiallyreliable demographics information from external sources (e.g., databaseproprietors), thus extending the coverage, accuracy, and/or completenessof the AMA's demographics-based audience measurements. Such access alsoenables the audience measurement entity to monitor persons who would nototherwise have joined an audience measurement panel. Any entity having anetwork-accessible database identifying demographics of a set ofindividuals may cooperate with the audience measurement entity. Suchentities may be referred to as “database proprietors” and includeentities such as Facebook, Google, Yahoo!, MSN, Twitter, Apple iTunes,Experian, etc.

To increase the likelihood that measured viewership is accuratelyattributed to the correct demographics, examples disclosed herein usedemographic information located in the audience measurement entity'srecords as well as demographic information located at one or moredatabase proprietors that maintain records or profiles of users havingaccounts therewith. In this manner, examples disclosed herein may beused to supplement demographic information maintained by a ratingsentity (e.g., an AME such as The Nielsen Company of Schaumburg, Ill.,United States of America, that collects media exposure measurementsand/or demographics) with demographic information from one or moredifferent database proprietors.

The use of demographic information from disparate data sources (e.g.,high-quality demographic information from the panels of an audiencemeasurement company and/or registered user data of web serviceproviders) results in improved reporting effectiveness of metrics forboth online and offline advertising campaigns. Example techniquesdisclosed herein use online registration data to identify demographicsof users and use server impression counts, tagging (also referred toherein as beaconing), and/or other techniques to track quantities ofimpressions attributable to those users. Online web service providerssuch as social networking sites (e.g., Facebook) and multi-serviceproviders (e.g., Yahoo!, Google, Experian, etc.) (collectively andindividually referred to herein as database proprietors) maintaindetailed demographic information (e.g., age, gender, geographiclocation, race, income level, education level, religion, etc.) collectedvia user registration processes. As used herein, an impression isdefined to be an event in which a home or individual is exposed tocorresponding media (e.g., content and/or an advertisement). Thus, animpression represents a home or an individual having been exposed tomedia (e.g., an advertisement, content, a group of advertisements,and/or a collection of content). In Internet advertising, a quantity ofimpressions or impression count is the total number of times media(e.g., content, an advertisement or advertisement campaign) has beenaccessed by a web population (e.g., the number of times the media isaccessed). As used herein, a demographic impression is defined to be animpression that is associated with a characteristic (e.g., a demographiccharacteristic) of the person exposed to the media.

Although such techniques for collecting media impressions are based onhighly accurate demographic information, in some instances collectedimpressions may be misattributed to the wrong person and, thus,associated with incorrect demographic information. For example, in ahousehold having multiple people that use the same client device (e.g.,the same computer, tablet, smart internet appliance, etc.), collectedimpressions from that client device may be misattributed to a member ofthe household that is not the current user of the client device. Thatis, when an online user visits a website and is exposed to anadvertisement on that site that has been tagged with beaconinstructions, there is a redirect to a server of a database proprietor(e.g., Facebook, Yahoo, Google, etc.). The database proprietor thenlooks into a most recent cookie set by the database proprietor in theweb browser of that client device. The database proprietor thenattributes the impression to the user account corresponding to thecookie value. For example, the cookie value is one that was previouslyset in the client device by the database proprietor to correspond to aparticular registered user account of the person that used the clientdevice to most recently log into the website of that databaseproprietor. After collecting and attributing the impression to the useraccount associated with the retrieved cookie value, the databaseproprietor aggregates the total collected impressions and the size ofthe unique audience based on demographics associated with user accountsfor all logged impressions. When this occurs over time and across manyhouseholds, a number of collected impressions are misattributed to thewrong demographic information because some people use client devicesafter another person (e.g., another household member) has logged into auser account registered with the database proprietor without loggingthemselves (e.g., the current audience member) in. As such, a cookiecorresponding to the previous person is still accessed from the clientdevice while the subsequent user of the client device (e.g., a user thatdid not log into a corresponding user account registered with thedatabase proprietor) accesses media on the client device which causesimpressions to be misattributed to the previous person associated withthe accessed cookie.

As used herein, a unique audience measure is based on audience membersdistinguishable from one another. That is, a particular audience memberexposed to particular media is measured as a single unique audiencemember regardless of how many times that audience member is exposed tothat particular media. If that particular audience member is exposedmultiple times to the same media, the multiple exposures for theparticular audience member to the same media is counted as only a singleunique audience member. In this manner, impression performance forparticular media is not disproportionately represented when a smallsubset of one or more audience members is exposed to the same media anexcessively large number of times while a larger number of audiencemembers is exposed fewer times or not at all to that same media. Bytracking exposures to unique audience members, a unique audience measuremay be used to determine a reach measure to identify how many uniqueaudience members are reached by media. In some examples, increasingunique audience and, thus, reach, is useful for advertisers wishing toreach a larger audience base.

As used herein, total impressions refers to the total number ofcollected impressions for particular media regardless of whethermultiple ones of those impressions are attributable to the same audiencemembers. That is, multiple impressions accounted for in the totalimpressions may be attributable to a same audience member.

Misattribution is a measurement error that typically arises whenimpressions are collected from a same client device that is shared bymultiple people in that a media impression caused by one person that iscurrently using the client device is incorrectly attributed (i.e.,misattributed) to another person that previously used the same clientdevice. Sharing of a client device can occur between two individualswho: (1) live in the same household, and/or (2) have access to the sameclient device. Misattribution occurs when, for a particular mediaexposure on a client device, a logged-in-user of a database proprietorservice (e.g., Facebook) is not the same as the current user of theclient device that is being exposed to the media. For example, if personA logs into the database proprietor's website in the morning on a clientdevice, but person B uses the same client device in the afternoonwithout logging in (e.g., without user A logging out) and is exposed tomedia tagged with beacon instructions, the database proprietorattributes the impression to person A since he/she was the last personto log into the database proprietor's site from that client device,while actually it was person B who was using the client device when themedia was presented.

Examples disclosed herein can be used to correct misattribution incollected impressions by applying a misattribution correction toimpression data obtained from a database proprietor (e.g., Facebook,Yahoo, Google, etc.) after a profile correction (e.g., a Decision Tree(DT) model) has been applied to the impression data. Examples disclosedherein may be implemented by an audience measurement entity (e.g., anyentity interested in measuring or tracking audience exposures toadvertisements, content, and/or any other media) in cooperation with anynumber of database proprietors such as online web services providers.Such database proprietors/online web services providers may be socialnetwork sites (e.g., Facebook, Twitter, MySpace, etc.), multi-servicesites (e.g., Yahoo!, Google, Axiom, Catalina, etc.), online retailersites (e.g., Amazon.com, Buy.com, etc.), credit reporting sites (e.g.,Experian), and/or any other web service(s) site that maintains userregistration records.

Example methods and/or articles of manufacture comprising computerreadable instructions disclosed herein may be used to receive, at afirst internet domain, a first request from a client device, the firstrequest indicative of access to media at the client device. In suchexamples, a response is sent from the first internet domain to theclient device. In such examples, the response instructs the clientdevice to send a second request to a second internet domain. In suchexamples, the second request is to be indicative of the access to themedia at the client device. In such examples, an impressions adjustmentfactor is determined for a first demographic group based on firstimpressions reported by the client device to the first internet domainand second impressions reported by the client device to the secondinternet domain. In such example, the first and second impressionscorrespond to the same media accessed on the client device. In suchexamples, a misattribution-corrected impressions count is determined forthe first demographic group based on the impressions adjustment factorand based on a second impressions count determined at the secondinternet domain for the first demographic group. In such examples, thesecond impressions count includes an error based on some of the secondimpressions being misattributed at the second internet domain to thefirst demographic group when the some of the second impressionscorrespond to a second demographic group.

In some examples, determining the misattribution-corrected impressioncount involves shifting an impression from the second impressions countcorresponding to the first demographic group to a third impressionscount corresponding to the second demographic group based on theimpressions adjustment factor. In some examples, the first impressionsare reported by the client device to an audience measurement entity atthe first internet domain that does not provide the media to the clientdevice, and a user of the client device is a panel member of theaudience measurement entity. In some examples, the second impressionsare reported by the client device to a social network service at thesecond internet domain to which a user of the client device issubscribed. In some examples, the impressions adjustment factor is tocorrect impression quantities having inaccuracies due to impressionsincorrectly attributed to demographic data not corresponding to personscorresponding to the impressions.

In some examples, determining the impressions adjustment factor involvessubtracting a first unique audience size determined by an audiencemeasurement entity at the first internet domain based on the firstimpressions from a second unique audience size determined by a databaseproprietor at the second internet domain based on the second impressionsto generate a difference. In such examples, the difference is divided bya total impressions count of the first impressions to determine theimpressions adjustment factor.

In some examples, the error in the second impressions count is based onan entity at the second internet domain incorrectly identifying a userof the client device as belonging to the first demographic group whenthe user belongs to the second demographic group. In such examples, themisattribution-corrected impressions count comprises fewer impressionsthan the second impression count based on shifting an impressioncorresponding to the user from the second impressions countcorresponding to the first demographic group to a third impressionscount corresponding to the second demographic group based on theimpressions adjustment factor.

In some examples, the misattribution-corrected impressions count isdetermined based on the impressions adjustment factor withoutcommunicating with individual online users about their online mediaaccess activities and without using survey responses from the onlineusers to determine the error. In some examples, network communicationbandwidth is conserved by not communicating with individual online usersabout their online media access activities and by not requesting surveyresponses from the online users to determine the error. In someexamples, computer processing resources are conserved by notcommunicating with individual online users about their online mediaaccess activities and by not requesting survey responses from the onlineusers to determine the error.

Example disclosed apparatus include an example impression collector toreceive, at a first internet domain, a first request from a clientdevice, the first request indicative of access to media at the clientdevice. The example impression collector is also to send, from the firstinternet domain, a response to the client device, the response toinstruct the client device to send a second request to a second internetdomain, the second request to be indicative of the access to the mediaat the client device. Such example apparatus also include an impressionsadjustment factor determiner to determine an impressions adjustmentfactor for a first demographic group based on first impressions reportedby the client device to the first internet domain and second impressionsreported by the client device to the second internet domain. In suchexamples, the first and second impressions correspond to the same mediaaccessed on the client device. Such example apparatus also includes animpressions corrector to determine a misattribution-correctedimpressions count for the first demographic group based on theimpressions adjustment factor and based on a second impressions countdetermined at the second internet domain for the first demographicgroup. In such examples, the second impressions count includes an errorbased on some of the second impressions being misattributed at thesecond internet domain to the first demographic group when the some ofthe second impressions correspond to a second demographic group.

In some examples, the impressions corrector is to determine themisattribution-corrected impressions count by shifting an impressionfrom the second impressions count corresponding to the first demographicgroup to a third impressions count corresponding to the seconddemographic group based on the impressions adjustment factor. In someexamples, the first impressions are reported by the client device to anaudience measurement entity at the first internet domain that does notprovide the media to the client device. In some examples, a user of theclient device is a panel member of the audience measurement entity. Insome examples, the second impressions are reported by the client deviceto a social network service at the second internet domain to which auser of the client device is subscribed.

In some examples, the impressions adjustment factor determiner is todetermine the impressions adjustment factor by subtracting a firstunique audience size determined by an audience measurement entity at thefirst internet domain based on the first impressions from a secondunique audience size determined by a database proprietor at the secondinternet domain based on the second impressions to generate adifference. In such examples, the difference is divided by a totalimpressions count of the first impressions.

In some examples, the impressions adjustment factor is to correctimpression quantities having inaccuracies due to impressions incorrectlyattributed to demographic data not corresponding to personscorresponding to the impressions. In some examples, the error in thesecond impressions count is based on an entity at the second internetdomain incorrectly identifying a user of the client device as belongingto the first demographic group when the user belongs to the seconddemographic group. In some examples, the misattribution-correctedimpressions count include fewer impressions than the second impressioncount based on shifting an impression corresponding to the user from thesecond impressions count corresponding to the first demographic group toa third impressions count corresponding to the second demographic groupbased on the impressions adjustment factor.

In some examples, the impressions corrector determines themisattribution-corrected impressions based on the impressions adjustmentfactor without communicating with individual online users about theironline media access activities and without using survey responses fromthe online users to determine the error. In some examples, bydetermining the misattribution-corrected impressions using theimpressions corrector, network communication bandwidth is conserved bynot communicating with individual online users about their online mediaaccess activities and by not requesting survey responses from the onlineusers to determine the error. In some examples, by determining themisattribution-corrected impressions using the impressions corrector,computer processing resources are conserved by not communicating withindividual online users about their online media access activities andby not requesting survey responses from the online users to determinethe error.

Example methods and/or articles of manufacture comprising computerreadable instructions disclosed herein may be used to receive, at afirst internet domain, a first request from a client device, the firstrequest indicative of access to media at the client device. In suchexamples, a response is sent from the first internet domain to theclient device. In such examples, the response is to instruct the clientdevice to send a second request to a second internet domain. In suchexamples, the second request is to be indicative of the access to themedia at the client device. In such examples, an audience adjustmentfactor is determined for a demographic group based on first impressionsreported by the client device to the first internet domain and secondimpressions reported by the client device to the second internet domain.In such examples, the first and second impressions correspond to thesame media accessed on the client device. In such examples, amisattribution-corrected unique audience size is determined for thedemographic group based on the audience adjustment factor and based on asecond unique audience size determined at the second internet domain forthe demographic group. In such examples, the second unique audience sizeincludes an error based on third impressions misattributed at the secondinternet domain to the demographic group when the third impressionscorrespond to another demographic group.

In some examples, determining the audience adjustment factor involvesdividing a third unique audience size corresponding to the firstimpressions by a fourth unique audience size corresponding to the secondimpressions. In some examples, determining the misattribution-correctedunique audience size for the demographic group involves dividing thesecond unique audience size by the audience adjustment factor. In someexamples, the first impressions are reported by the client device to anaudience measurement entity at the first internet domain that does notprovide the media to the client device, and a user of the client deviceis a panel member of the audience measurement entity. In some examples,the second impressions are reported by the client device to a socialnetwork service at the second internet domain to which a user of theclient device is subscribed. In some examples, the audience adjustmentfactor is to correct unique audience size values having inaccuracies dueto impressions incorrectly attributed to demographic data notcorresponding to persons corresponding to the impressions.

In some examples, the error in the second unique audience size is basedon an entity at the second internet domain incorrectly identifying auser of the client device as belonging to the demographic group when theuser belongs to the another demographic group. In some such examples,the misattribution-corrected unique audience size is different than thesecond unique audience size based on dividing the second unique audiencesize by the audience adjustment factor.

In some examples, the misattribution-corrected unique audience size isdetermined based on the audience adjustment factor without communicatingwith individual online users about their online media access habits andwithout using survey responses from the online users to determine theerror. In some examples, network communication bandwidth is conserved bynot communicating with individual online users about their online mediaaccess habits and by not requesting survey responses from the onlineusers to determine the error. In some examples, computer processingresources are conserved by not communicating with individual onlineusers about their online media access habits and by not requestingsurvey responses from the online users to determine the error.

Example disclosed apparatus include an example impression collector toreceive, at a first internet domain, a first request from a clientdevice. In such examples, the first request is indicative of access tomedia at the client device. The example impression collector is also tosend, from the first internet domain, a response to the client device.In such examples, the response is to instruct the client device to senda second request to a second internet domain. In such examples, thesecond request is to be indicative of the access to the media at theclient device. Such example apparatus also include an audienceadjustment factor determiner to determine an audience adjustment factorfor a demographic group based on first impressions reported by theclient device to the first internet domain and second impressionsreported by the client device to the second internet domain. In suchexamples, the first and second impressions correspond to the same mediaaccessed on the client device. Such example apparatus also include aunique audience corrector to determine a misattribution-corrected uniqueaudience size for the demographic group based on the audience adjustmentfactor and based on a second unique audience size determined at thesecond internet domain for the demographic group. In such examples, thesecond unique audience size includes an error based on third impressionsmisattributed at the second internet domain to the demographic groupwhen the third impressions correspond to another demographic group.

In some examples, the audience adjustment factor determiner is todetermine the audience adjustment factor by dividing a third uniqueaudience size corresponding to the first impressions by a fourth uniqueaudience size corresponding to the second impressions. In some examples,the unique audience corrector is to determine themisattribution-corrected unique audience size for the demographic groupby dividing the second unique audience size by the audience adjustmentfactor. In some examples, the first impressions are reported by theclient device to an audience measurement entity at the first internetdomain that does not provide the media to the client device, and a userof the client device is a panel member of the audience measuremententity. In some examples, the second impressions are reported by theclient device to a social network service at the second internet domainto which a user of the client device is subscribed. In some examples,the audience adjustment factor is to correct unique audience size valueshaving inaccuracies due to impressions incorrectly attributed todemographic data not corresponding to persons corresponding to theimpressions.

In some examples, the error in the second unique audience size is basedon an entity at the second internet domain incorrectly identifying auser of the client device as belonging to the demographic group when theuser belongs to the another demographic group. In some such examples,the misattribution-corrected unique audience size comprising dividingthe second unique audience size by the audience adjustment factor.

In some examples, the unique audience corrector is to determine themisattribution-corrected unique audience size based on the audienceadjustment factor without communicating with individual online usersabout their online media access habits and without using surveyresponses from the online users to determine the error. In someexamples, by determining the misattribution-corrected unique audiencesize, the unique audience corrector conserves network communicationbandwidth by not communicating with individual online users about theironline media access habits and by not requesting survey responses fromthe online users to determine the error. In some examples, bydetermining the misattribution-corrected unique audience size, theunique audience corrector conserves computer processing resources by notcommunicating with individual online users about their online mediaaccess habits and by not requesting survey responses from the onlineusers to determine the error.

FIG. 1 illustrates an example client device 102 that reports audienceimpressions for media to impression collection entities 104 tofacilitate identifying total impressions and sizes of unique audiencesexposed to different media. As used herein, the term impressioncollection entity refers to any entity that collects impression data.The client device 102 of the illustrated example may be any devicecapable of accessing media over a network. For example, the clientdevice 102 may be a computer, a tablet, a mobile device, a smarttelevision, or any other Internet-capable device or appliance. Examplesdisclosed herein may be used to collect impression information for anytype of media including content and/or advertisements. Media may includeadvertising and/or content such as web pages, streaming video, streamingaudio, movies, and/or any other type of content and/or advertisementdeliver via satellite, broadcast, cable television, radio frequency (RF)terrestrial broadcast, Internet (e.g., internet protocol television(IPTV)), television broadcasts, radio broadcasts and/or any othervehicle for delivering media. In some examples, media includesuser-generated media that is, for example, uploaded to media uploadsites such as YouTube and subsequently downloaded and/or streamed by oneor more client devices for playback. Media may also includeadvertisements. Advertisements are typically distributed with content(e.g., programming). Traditionally, content is provided at little or nocost to the audience because it is subsidized by advertisers that pay tohave their advertisements distributed with the content. As used herein,“media” refers collectively and/or individually to content and/oradvertisement(s) of any type(s).

In the illustrated example, the client device 102 employs a web browserand/or applications (e.g., apps) to access media, some of which includeinstructions that cause the client device 102 to report media monitoringinformation to one or more of the impression collection entities 104.That is, when the client device 102 of the illustrated example accessesmedia, a web browser and/or application of the client device 102executes instructions in the media to send a beacon request orimpression request 108 to one or more of the impression collectionentities 104 via, for example, the Internet 110. The beacon requests 108of the illustrated example include information about accesses to mediaat the client device 102. Such beacon requests 108 allow monitoringentities, such as the impression collection entities 104, to collectimpressions for different media accessed via the client device 102. Inthis manner, the impression collection entities 104 can generate largeimpression quantities for different media (e.g., different contentand/or advertisement campaigns).

The impression collection entities 104 of the illustrated exampleinclude an example audience measurement entity (AME) 114 and an exampledatabase proprietor (DP) 116. In the illustrated example, the AME 114does not provide the media to the client device 102 and is a trusted(e.g., neutral) third party (e.g., The Nielsen Company, LLC) forproviding accurate media access statistics. In the illustrated example,the database proprietor 116 is one of many database proprietors thatoperates on the Internet to provide services to large numbers ofsubscribers. Such services may be email services, social networkingservices, news media services, cloud storage services, streaming musicservices, streaming video services, online retail shopping services,credit monitoring services, etc. Example database proprietors includesocial network sites (e.g., Facebook, Twitter, MySpace, etc.),multi-service sites (e.g., Yahoo!, Google, etc.), online retailer sites(e.g., Amazon.com, Buy.com, etc.), credit reporting services (e.g.,Experian) and/or any other web service(s) site that maintains userregistration records. In examples disclosed herein, the databaseproprietor 116 maintains user account records corresponding to usersregistered for Internet-based services provided by the databaseproprietors. That is, in exchange for the provision of services,subscribers register with the database proprietor 116. As part of thisregistration, the subscribers provide detailed demographic informationto the database proprietor 116. Demographic information may include, forexample, gender, age, ethnicity, income, home location, education level,occupation, etc. In the illustrated example, the database proprietor 116sets a device/user identifier (e.g., an identifier described below inconnection with FIG. 2) on a subscriber's client device 102 that enablesthe database proprietor 116 to identify the subscriber.

In the illustrated example, when the database proprietor 116 receives abeacon/impression request 108 from the client device 102, the databaseproprietor 116 requests the client device 102 to provide the device/useridentifier that the database proprietor 116 had previously set for theclient device 102. The database proprietor 116 uses the device/useridentifier corresponding to the client device 102 to identifydemographic information in its user account records corresponding to thesubscriber of the client device 102. In this manner, the databaseproprietor 116 can generate demographic impressions by associatingdemographic information with an audience impression for the mediaaccessed at the client device 102. As explained above, a demographicimpression is an impression that is associated with a characteristic(e.g., a demographic characteristic) of the person exposed to the media.

In the illustrated example, the AME 114 establishes an AME panel ofusers who have agreed to provide their demographic information and tohave their Internet browsing activities monitored. When an individualjoins the AME panel, the person provides detailed information concerningthe person's identity and demographics (e.g., gender, age, ethnicity,income, home location, occupation, etc.) to the AME 114. The AME 114sets a device/user identifier (e.g., an identifier described below inconnection with FIG. 2) on the person's client device 102 that enablesthe AME 114 to identify the panelist. An AME panel may be across-platform home television/computer (TVPC) panel built andmaintained by the AME 114. In other examples, the AME panel may be acomputer panel or internet-device panel without corresponding to atelevision audience panel. In yet other examples, the AME panel may be across-platform radio/computer panel and/or a panel formed for othermediums.

In the illustrated example, when the AME 114 receives a beacon request108 from the client device 102, the AME 114 requests the client device102 to provide the AME 114 with the device/user identifier that the AME114 previously set in the client device 102. The AME 114 uses thedevice/user identifier corresponding to the client device 102 toidentify demographic information in its user AME panelist recordscorresponding to the panelist of the client device 102. In this manner,the AME 114 can generate demographic impressions by associatingdemographic information with an audience impression for the mediaaccessed at the client device 102.

In the illustrated example, the client device 102 is used in an examplehousehold 120 in which household members 122 and 124 (identified assubscriber A 122 and subscriber B 124) are subscribers of aninternet-based service offered by the database proprietor 116. In theillustrated example, subscriber A 122 and subscriber B 124 share theclient device 102 to access the internet-based service of the databaseproprietor 116 and to access other media via the Internet 110. In theillustrated example, when the database proprietor 116 receives abeacon/impression request 108 for media accessed via the client device102, the database proprietor 116 logs an impression for the media accessas corresponding to the subscriber 122, 124 of the household 120 thatmost recently logged into the database proprietor 116. Misattributionsof impressions logged by the database proprietor 116 are likely to occurin circumstances similar to the example household 120 of FIG. 1 in whichmultiple people in a household share a client device. For example, ifthe subscriber A 122 logs into a service of the database proprietor 116on the client device 102, and the subscriber B 124 subsequently uses theclient device 102 without logging in to the service of the databaseproprietor 116, the database proprietor 116 attributes logged impressionto the subscriber A 122 even though the use is actually by subscriber B124 because the subscriber A 122 was the last person to log into thedatabase proprietor 116 and, thus, the subscriber A 122 was mostrecently identified by the database proprietor 116 as the subscriberusing the client device 102. As such, even though the subscriber B 124was subsequently using the client device 102, impressions logged by thedatabase proprietor 116 during such use are not attributed to thecorrect person (i.e., the subscriber B 124) because the most recentlogin detected by the database proprietor 116 corresponded to thesubscriber A 122. In the illustrated example, logins are used by thedatabase proprietor 116 to identify subscribers using particular devicesby associating device/user identifiers on the client devices withsubscriber accounts at the database proprietor 116 corresponding tousernames used during the logins. As such, the database proprietor 116assumes that the most recent login is indicative of a subscriber usingthe client device 102 until another login event is received at thedatabase proprietor 116 that identifies a different subscriber. Suchassumptions based on the most recent login lead to the above-describedmisattributions.

FIG. 2 illustrates an example communication flow diagram of an examplemanner in which the AME 114 and the database proprietor 116 of FIG. 1can collect impressions and demographic information based on the clientdevice 102 reporting impressions to the AME 114 and the databaseproprietor 116. FIG. 2 also shows an example misattribution corrector202. The misattribution corrector 202 of the illustrated example is tocorrect unique audience sizes and impression counts that are based onimpressions reported by client devices (e.g., the client device 102) andfor which the database proprietor 116 has misattributed some of thoseimpressions to incorrect people and, thus, incorrect demographicinformation. The example chain of events shown in FIG. 2 occurs when theclient device 102 accesses media for which the client device 102 reportsan impression to the AME 114 and the database proprietor 116. In someexamples, the client device 102 reports impressions for accessed mediabased on instructions (e.g., beacon instructions) embedded in the mediathat instruct the client device 102 (e.g., instruct a web browser or anapp in the client device 102) to send beacon/impression requests (e.g.,the beacon/impression requests 108 of FIG. 1) to the AME 114 and/or thedatabase proprietor 116. In such examples, the media having the beaconinstructions is referred to as tagged media. In other examples, theclient device 102 reports impressions for accessed media based oninstructions embedded in apps or web browsers that execute on the clientdevice 102 to send beacon/impression requests (e.g., thebeacon/impression requests 108 of FIG. 1) to the AME 114, and/or thedatabase proprietor 116 for corresponding media accessed via those appsor web browsers. In any case, the beacon/impression requests (e.g., thebeacon/impression requests 108 of FIG. 1) include device/useridentifiers (e.g., AME IDs and/or DP IDs) as described further below toallow the corresponding AME 114 and/or database proprietor 116 toassociate demographic information with resulting logged impressions.

In the illustrated example, the client device 102 accesses media 206that is tagged with beacon instructions 208. The beacon instructions 208cause the client device 102 to send a beacon/impression request 212 toan AME impressions collector 218 when the client device 102 accesses themedia 206. For example, a web browser and/or app of the client device102 executes the beacon instructions 208 in the media 206 which instructthe browser and/or app to generate and send the beacon/impressionrequest 212. In the illustrated example, the client device 102 sends thebeacon/impression request 212 to the AME impression collector 218 usingan HTTP (hypertext transfer protocol) request addressed to the URL(uniform resource locator) of the AME impressions collector 218 at, forexample, a first internet domain of the AME 114. The beacon/impressionrequest 212 of the illustrated example includes a media identifier 213(e.g., an identifier that can be used to identify content, anadvertisement, and/or any other media) corresponding to the media 206.In some examples, the beacon/impression request 212 also includes a siteidentifier (e.g., a URL) of the website that served the media 206 to theclient device 102 and/or a host website ID (e.g., www.acme.com) of thewebsite that displays or presents the media 206. In the illustratedexample, the beacon/impression request 212 includes a device/useridentifier 214. In the illustrated example, the device/user identifier214 that the client device 102 provides in the beacon impression request212 is an AME ID because it corresponds to an identifier that the AME114 uses to identify a panelist corresponding to the client device 102.In other examples, the client device 102 may not send the device/useridentifier 214 until the client device 102 receives a request for thesame from a server of the AME 114 (e.g., in response to, for example,the AME impressions collector 218 receiving the beacon/impressionrequest 212).

In some examples, the device/user identifier 214 may be a deviceidentifier (e.g., an international mobile equipment identity (IMEI), amobile equipment identifier (MEID), a media access control (MAC)address, etc.), a web browser unique identifier (e.g., a cookie), a useridentifier (e.g., a user name, a login ID, etc.), an Adobe Flash® clientidentifier, identification information stored in an HTML5 datastore,and/or any other identifier that the AME 114 stores in association withdemographic information about users of the client devices 102. When theAME 114 receives the device/user identifier 214, the AME 114 can obtaindemographic information corresponding to a user of the client device 102based on the device/user identifier 214 that the AME 114 receives fromthe client device 102. In some examples, the device/user identifier 214may be encrypted (e.g., hashed) at the client device 102 so that only anintended final recipient of the device/user identifier 214 can decryptthe hashed identifier 214. For example, if the device/user identifier214 is a cookie that is set in the client device 102 by the AME 114, thedevice/user identifier 214 can be hashed so that only the AME 114 candecrypt the device/user identifier 214. If the device/user identifier214 is an IMEI number, the client device 102 can hash the device/useridentifier 214 so that only a wireless carrier (e.g., the databaseproprietor 116) can decrypt the hashed identifier 214 to recover theIMEI for use in accessing demographic information corresponding to theuser of the client device 102. By hashing the device/user identifier214, an intermediate party (e.g., an intermediate server or entity onthe Internet) receiving the beacon request cannot directly identify auser of the client device 102.

In response to receiving the beacon/impression request 212, the AMEimpressions collector 218 logs an impression for the media 206 bystoring the media identifier 213 contained in the beacon/impressionrequest 212. In the illustrated example of FIG. 2, the AME impressionscollector 218 also uses the device/user identifier 214 in thebeacon/impression request 212 to identify AME panelist demographicinformation corresponding to a panelist of the client device 102. Thatis, the device/user identifier 214 matches a user ID of a panelistmember (e.g., a panelist corresponding to a panelist profile maintainedand/or stored by the AME 114). In this manner, the AME impressionscollector 218 can associate the logged impression with demographicinformation of a panelist corresponding to the client device 102.

In some examples, the beacon/impression request 212 may not include thedevice/user identifier 214 if, for example, the user of the clientdevice 102 is not an AME panelist. In such examples, the AME impressionscollector 218 logs impressions regardless of whether the client device102 provides the device/user identifier 214 in the beacon/impressionrequest 212 (or in response to a request for the identifier 214). Whenthe client device 102 does not provide the device/user identifier 214,the AME impressions collector 218 will still benefit from logging animpression for the media 206 even though it will not have correspondingdemographics. For example, the AME 114 may still use the loggedimpression to generate a total impressions count and/or a frequency ofimpressions (e.g., an impressions frequency) for the media 206.Additionally or alternatively, the AME 114 may obtain demographicsinformation from the database proprietor 116 for the logged impressionif the client device 102 corresponds to a subscriber of the databaseproprietor 116.

In the illustrated example of FIG. 2, to compare or supplement panelistdemographics (e.g., for accuracy or completeness) of the AME 114 withdemographics from one or more database proprietors (e.g., the databaseproprietor 116), the AME impressions collector 218 returns a beaconresponse message 222 (e.g., a first beacon response) to the clientdevice 102 including an HTTP “302 Found” re-direct message and a URL ofa participating database proprietor 116 at, for example, a secondinternet domain. In the illustrated example, the HTTP “302 Found”re-direct message in the beacon response 222 instructs the client device102 to send a second beacon request 226 to the database proprietor 116.In other examples, instead of using an HTTP “302 Found” re-directmessage, redirects may be implemented using, for example, an iframesource instruction (e.g., <iframe src=“ ”>) or any other instructionthat can instruct a client device to send a subsequent beacon request(e.g., the second beacon request 226) to a participating databaseproprietor 116. In the illustrated example, the AME impressionscollector 218 determines the database proprietor 116 specified in thebeacon response 222 using a rule and/or any other suitable type ofselection criteria or process. In some examples, the AME impressionscollector 218 determines a particular database proprietor to which toredirect a beacon request based on, for example, empirical dataindicative of which database proprietor is most likely to havedemographic data for a user corresponding to the device/user identifier214. In some examples, the beacon instructions 208 include a predefinedURL of one or more database proprietors to which the client device 102should send follow up beacon requests 226. In other examples, the samedatabase proprietor is always identified in the first redirect message(e.g., the beacon response 222).

In the illustrated example of FIG. 2, the beacon/impression request 226may include a device/user identifier 227 that is a DP ID because it isused by the database proprietor 116 to identify a subscriber of theclient device 102 when logging an impression. In some instances (e.g.,in which the database proprietor 116 has not yet set a DP ID in theclient device 102), the beacon/impression request 226 does not includethe device/user identifier 227. In some examples, the DP ID is not sentuntil the DP requests the same (e.g., in response to thebeacon/impression request 226). In some examples, the device/useridentifier 227 is a device identifier (e.g., an international mobileequipment identity (IMEI), a mobile equipment identifier (MEID), a mediaaccess control (MAC) address, etc.), a web browser unique identifier(e.g., a cookie), a user identifier (e.g., a user name, a login ID,etc.), an Adobe Flash® client identifier, identification informationstored in an HTML5 datastore, and/or any other identifier that thedatabase proprietor 116 stores in association with demographicinformation about subscribers corresponding to the client devices 102.When the database proprietor 116 receives the device/user identifier227, the database proprietor 116 can obtain demographic informationcorresponding to a user of the client device 102 based on thedevice/user identifier 227 that the database proprietor 116 receivesfrom the client device 102. In some examples, the device/user identifier227 may be encrypted (e.g., hashed) at the client device 102 so thatonly an intended final recipient of the device/user identifier 227 candecrypt the hashed identifier 227. For example, if the device/useridentifier 227 is a cookie that is set in the client device 102 by thedatabase proprietor 116, the device/user identifier 227 can be hashed sothat only the database proprietor 116 can decrypt the device/useridentifier 227. If the device/user identifier 227 is an IMEI number, theclient device 102 can hash the device/user identifier 227 so that only awireless carrier (e.g., the database proprietor 116) can decrypt thehashed identifier 227 to recover the IMEI for use in accessingdemographic information corresponding to the user of the client device102. By hashing the device/user identifier 227, an intermediate party(e.g., an intermediate server or entity on the Internet) receiving thebeacon request cannot directly identify a user of the client device 102.For example, if the intended final recipient of the device/useridentifier 227 is the database proprietor 116, the AME 114 cannotrecover identifier information when the device/user identifier 227 ishashed by the client device 102 for decrypting only by the intendeddatabase proprietor 116.

In some examples that use cookies as the device/user identifier 227,when a user deletes a database proprietor cookie from the client device102, the database proprietor 116 sets the same cookie value in theclient device 102 the next time the user logs into a service of thedatabase proprietor 116. In such examples, the cookies used by thedatabase proprietor 116 are registration-based cookies, which facilitatesetting the same cookie value after a deletion of the cookie value hasoccurred on the client device 102. In this manner, the databaseproprietor 116 can collect impressions for the client device 102 basedon the same cookie value over time to generate unique audience (UA)sizes while eliminating or substantially reducing the likelihood that asingle unique person will be counted as two or more separate uniqueaudience members.

Although only a single database proprietor 116 is shown in FIGS. 1 and2, the impression reporting/collection process of FIGS. 1 and 2 may beimplemented using multiple database proprietors. In some such examples,the beacon instructions 208 cause the client device 102 to sendbeacon/impression requests 226 to numerous database proprietors. Forexample, the beacon instructions 208 may cause the client device 102 tosend the beacon/impression requests 226 to the numerous databaseproprietors in parallel or in daisy chain fashion. In some suchexamples, the beacon instructions 208 cause the client device 102 tostop sending beacon/impression requests 226 to database proprietors oncea database proprietor has recognized the client device 102. In otherexamples, the beacon instructions 208 cause the client device 102 tosend beacon/impression requests 226 to database proprietors so thatmultiple database proprietors can recognize the client device 102 andlog a corresponding impression. In any case, multiple databaseproprietors are provided the opportunity to log impressions and providecorresponding demographics information if the user of the client device102 is a subscriber of services of those database proprietors.

In some examples, prior to sending the beacon response 222 to the clientdevice 102, the AME impressions collector 218 replaces site IDs (e.g.,URLs) of media provider(s) that served the media 206 with modified siteIDs (e.g., substitute site IDs) which are discernable only by the AME114 to identify the media provider(s). In some examples, the AMEimpressions collector 218 may also replace a host website ID (e.g.,www.acme.com) with a modified host site ID (e.g., a substitute host siteID) which is discernable only by the AME 114 as corresponding to thehost website via which the media 206 is presented. In some examples, theAME impressions collector 218 also replaces the media identifier 213with a modified media identifier 213 corresponding to the media 206. Inthis way, the media provider of the media 206, the host website thatpresents the media 206, and/or the media identifier 213 are obscuredfrom the database proprietor 116, but the database proprietor 116 canstill log impressions based on the modified values which can later bedeciphered by the AME 114 after the AME 114 receives logged impressionsfrom the database proprietor 116. In some examples, the AME impressionscollector 218 does not send site IDs, host site IDS, the mediaidentifier 213 or modified versions thereof in the beacon response 222.In such examples, the client device 102 provides the original,non-modified versions of the media identifier 213, site IDs, host IDs,etc. to the database proprietor 116.

In the illustrated example, the AME impression collector 218 maintains amodified ID mapping table 228 that maps original site IDs with modified(or substitute) site IDs, original host site IDs with modified host siteIDs, and/or maps modified media identifiers to the media identifierssuch as the media identifier 213 to obfuscate or hide such informationfrom database proprietors such as the database proprietor 116. Also inthe illustrated example, the AME impressions collector 218 encrypts allof the information received in the beacon/impression request 212 and themodified information to prevent any intercepting parties from decodingthe information. The AME impressions collector 218 of the illustratedexample sends the encrypted information in the beacon response 222 tothe client device 102 so that the client device 102 can send theencrypted information to the database proprietor 116 in thebeacon/impression request 226. In the illustrated example, the AMEimpressions collector 218 uses an encryption that can be decrypted bythe database proprietor 116 site specified in the HTTP “302 Found”re-direct message.

Periodically or aperiodically, the impression data collected by thedatabase proprietor 116 is provided to a DP impressions collector 230 ofthe AME 114 as, for example, batch data. As discussed above, someimpressions logged by the client device 102 to the database proprietor116 are misattributed by the database proprietor 116 to a wrongsubscriber and, thus, to incorrect demographic information. During adata collecting and merging process to combine demographic andimpression data from the AME 114 and the database proprietor 116,demographics of impressions logged by the AME 114 for the client device102 will not correspond to demographics of impressions logged by thedatabase proprietor 116 because the database proprietor 116 hasmisattributed some impressions to the incorrect demographic information.Examples disclosed herein may be used to determine an impressionsadjustment factor to correct/adjust impression-based data (e.g., totalimpressions and unique audience size) provided by the databaseproprietor 116.

Additional examples that may be used to implement the beacon instructionprocesses of FIG. 2 are disclosed in Mainak et al., U.S. Pat. No.8,370,489, which is hereby incorporated herein by reference in itsentirety. In addition, other examples that may be used to implement suchbeacon instructions are disclosed in Blumenau, U.S. Pat. No. 6,108,637,which is hereby incorporated herein by reference in its entirety.

In the example of FIG. 2, the AME 114 includes the examplemisattribution corrector 202 to correct unique audience values andimpression counts that are based on impressions reported by clientdevices (e.g., the client device 102) for which the database proprietor116 has misattributed some of the impressions to incorrect demographicinformation. The misattribution corrector 202 of the illustrated exampleis provided with an example audience adjustment factor determiner 232,an example impressions adjustment factor determiner 234, an exampleunique audience corrector 236, and an example impressions corrector 238.

The example audience adjustment factor determiner 232 of FIG. 2 isprovided to calculate a unique audience (UA) adjustment factorrepresentative of an inaccurate UA size that is based on misattributedimpressions relative to a UA size that is based on accurately attributedimpressions. As discussed above, misattribution occurs when the databaseproprietor 116 identifies the wrong person as being a current user ofthe client device 102 when the client device reports an impression foraccessed media to the database proprietor 116. The example impressionsadjustment factor determiner 234 is provided to calculate an impressionsadjustment factor representative of an amount of misattributedimpressions relative to an amount of correctly attributed impressions.

The example unique audience corrector 236 of FIG. 2 is provided tocorrect unique audience sizes or quantities by applying the impressionsadjustment factor (determined by the impressions adjustment factordeterminer 234) to total unique audience sizes corresponding to totalimpressions collected by the AME 114. The example impressions corrector238 is provided to correct an impressions count by applying theimpressions adjustment factor (determined by the impressions adjustmentfactor determiner 234) to the total number of impressions collected bythe AME 114.

Although the misattribution corrector 202 is shown in the illustratedexample as being located in the AME 114, the misattribution corrector202 may alternatively be located at any other location such as at thedatabase proprietor 116 or at any other suitable location (e.g.,location(s) separate from the AME 114 and the database proprietor 116).In addition, although the AME impressions collector 218, the modified IDmap 228, and the DP impressions collector 230 are shown separate fromthe misattribution corrector 202, one or more of the AME impressionscollector 218, the modified ID map 228, and/or the DP impressionscollector 230 may be implemented in the misattribution corrector 202.

While an example manner of implementing the example misattributioncorrector 202, the example impressions collector 218, the examplemodified ID map 228, the example DP impressions collector 230, theexample audience adjustment factor determiner 232, the exampleimpressions adjustment factor determiner 234, the example uniqueaudience corrector 236, and the example impressions corrector 238 isillustrated in FIG. 2, one or more of the elements, processes and/ordevices illustrated in FIG. 2 may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. Further, theexample misattribution corrector 202, the example AME impressionscollector 218, the example modified ID map 228, the example DPimpressions collector 230, the example audience adjustment factordeterminer 232, the example impressions adjustment factor determiner234, the example unique audience corrector 236, and/or the exampleimpressions corrector 238 of FIG. 2 may be implemented by hardware,software, firmware and/or any combination of hardware, software, and/orfirmware. Thus, for example, any of the example misattribution corrector202, the example AME impressions collector 218, the example modified IDmap 228, the example DP impressions collector 230, the example audienceadjustment factor determiner 232, the example impressions adjustmentfactor determiner 234, the example unique audience corrector 236, and/orthe example impressions corrector 238 could be implemented by one ormore analog or digital circuit(s), logic circuits, programmableprocessor(s), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)), and/or field programmable logicdevice(s) (FPLD(s)). When reading any of the apparatus or system claimsof this patent to cover a purely software and/or firmwareimplementation, at least one of the example misattribution corrector202, the example AME impressions collector 218, the example modified IDmap 228, the example DP impressions collector 230, the example audienceadjustment factor determiner 232, the example impressions adjustmentfactor determiner 234, the example unique audience corrector 236, and/orthe example impressions corrector 238 is/are hereby expressly defined toinclude a tangible computer readable storage device or storage disk suchas a memory, a digital versatile disk (DVD), a compact disk (CD), aBlu-ray disk, etc. storing the software and/or firmware. Further still,the example misattribution corrector 202, the example impressionscollector 218, the example modified ID map 228, the example DPimpressions collector 230, the example audience adjustment factordeterminer 232, the example impressions adjustment factor determiner234, the example unique audience corrector 236, and/or the exampleimpressions corrector 238 of FIG. 2 may include one or more elements,processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 2, and/or may include more than one of any or all ofthe illustrated elements, processes and devices.

Examples disclosed herein to correct impression-based data (e.g., totalimpressions and unique audience size) provided by the databaseproprietor 116 involve generating an adjustment factor based onimpressions collected by the AME 116 and correctly attributed todemographic information for corresponding AME panelists. Themisattribution corrector 202 of FIG. 2 may be implemented using theexample techniques below to correct impression-based data that is basedon impressions of which some are misattributed to the wrong demographicinformation by the database proprietor 116.

Examples disclosed herein involve using impressions logged by the AME114 in association with demographic data collected from AME panelmembers to calculate an audience adjustment factor using exampleEquation 1 below and an impression adjustment factor using exampleEquation 2 below. Audience adjustment factors determined using exampleEquation 1 can be used to correct unique audience size values havinginaccuracies due to misattributions of impressions by databaseproprietors. Impression adjustment factors determined using exampleEquation 2 below can be used to correct impression quantities havinginaccuracies due to misattributions of impressions by databaseproprietors.

In the illustrated example of FIG. 2, the audience adjustment factordeterminer 232 can use example Equation 1 below to determine an audienceadjustment factor (f_(i,j)) for persons in a demographic group (j) thataccessed media (i).

$\begin{matrix}{f_{i,j} = \frac{\Sigma_{i,j}\mspace{14mu} F_{i,j}}{\Sigma_{i,j}\mspace{14mu} T_{i,j}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

In example Equation 1 above, f_(i,j) is the adjustment factor for aunique audience (UA) size of a particular demographic group (j) thataccessed media (i), F_(i,j) is a database proprietor (DP) UA count ofthe number of AME panelists of the AME 114 that the database proprietor116 observes (e.g., recognizes, identifies, logs impressions for, etc.)in the demographic group (j) as accessing the media (i), and T_(i,j) isan AME UA count of AME panelists that the AME 114 observes in thedemographic group (j) as accessing the media (i).

In the illustrated example of FIG. 2, the impressions adjustment factordeterminer 234 employs example Equation 2 below to determine animpressions adjustment factor (k_(i,j)) for persons in a demographicgroup (j) that accessed media (i).

$\begin{matrix}{k_{i,j} = {{\frac{\Sigma_{i,j}\mspace{14mu} Q_{i,j}}{\Sigma_{i}S_{i}} - \frac{\Sigma_{i,j}\mspace{14mu} R_{i,j}}{\Sigma_{i}S_{i}}} = \frac{{\Sigma_{i,j}\mspace{14mu} Q_{i,j}} - {\Sigma_{i,j}\mspace{14mu} R_{i,j}}}{\Sigma_{i}S_{i}}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

In example Equation 2 above, k_(i,j) is the impressions adjustmentfactor for impressions logged for a particular demographic group (j)that accessed media (i), R_(i,j) is a DP UA count of the number of AMEpanelists of the AME 114 that the database proprietor 116 observes(e.g., recognizes, identifies, logs impressions for, etc.) in thedemographic group (j) as accessing the media (i), Q_(i,j) is an AME UAcount of AME panelists that the AME 114 observes in the demographicgroup (j) as accessing the media (i), and S_(i) is the total AMEimpressions of AME panelists (summed across all demographic groups) thataccessed media (i).

FIG. 3 illustrates an example table 300 with example AME impressions 302collected by the AME 114 and example DP impressions 304 collected by thedatabase proprietor 116 for different demographic groups (e.g., femalesyounger than 50 years (F<50), females 50 years old and older (F>=50),males younger than 50 years (M<50), and males 50 years old and older(M>=50)). The example AME impressions 304 and the example DP impressions304 shown in the example table 300 are development or test impressionsthat are collected by the AME 114 and the DP 116 during an adjustmentfactors development phase (e.g., an adjustment factors development phase802 of FIG. 8) with the purpose of calculating adjustment factors (e.g.,audience adjustment (AA) factors 402 of FIGS. 4 and 6 and impressionadjustment (IA) factors 502 of FIGS. 5 and 6) that can be subsequentlyused on large logs of real impressions collected by the databaseproprietor 116 to correct for impression misattributions that affectunique audience sizes and impression counts that are generated using thedatabase proprietor's logged impressions.

In the illustrated example of FIG. 3, the DP impressions 304 have anexample misattribution error 308 for impression #9 (IMP #9). That is,the impressions 302, 304 collected by both the AME 114 and the databaseproprietor 116 are based on client devices (e.g., the client device 102)having users that are both (1) AME panelists of the AME 114 and (2)registered subscribers of the database proprietor 116. When the AME 114logs an impression based on, for example, the beacon/impression request212 of FIG. 2 from a panelist of the AME 114, the AME 114 logs anaccurate impression. In the illustrated example of FIG. 3, such AMEimpressions 302 are also referred to as truth impressions 302 becausethe AME 114 regards them as correctly associated with correspondingdemographic information of the current user of the client device 102. Insome examples, to assure the accuracy of the AME impressions 302, theAME 114 incentivizes (e.g., through cash or other rewards) AME panelmembers to login to an AME website whenever the AME panel members beginusing a client device 102. In this manner, the AME 114 can accuratelyset and/or associate an AME ID (e.g., the device/user identifier 214 ofFIG. 2) with an AME panelist that is currently using the client device102.

Unlike the known accuracy, or truth, of the AME impressions 302, thereare no assurances that the DP impressions 304 are accurately associatedwith correct demographic information. That is, subscribers of thedatabase proprietor 116 may not be incentivized to login to a website orservice of the database proprietor 116 when the subscribers begin usinga client device 102. As such, the database proprietor 116 is sometimesunable to accurately set and/or associate a DP ID (e.g., the device/useridentifier 227 of FIG. 2) with a person that is currently using theclient device 102. The misattributions present in the development ortest impressions of the table 300 of FIG. 3 are representative of thetypes of misattributions that the database proprietor 116 is likely tomake when logging impressions for persons that may or may not be AMEpanelists and/or may or may not be known to the database proprietor 116.Therefore, calculating adjustment factors based on the developmentimpressions of the table 300 of FIG. 3 results in adjustment factorsthat can be used to correct for misattributions in impressionssubsequently collected by the database proprietor 116 for other users.

In the illustrated example of FIG. 3, the example misattribution error308 at impression #9 is created when the database proprietor 116mis-recognizes an impression reported by the client device 102 (e.g.,via the beacon/impression request 226 of FIG. 2) as being associatedwith demographic information for a male (M) of age 30. In the exampletable 300 of FIG. 3, a correct demographic impression at IMP #9 loggedby the AME 114 for the same person (e.g., via the beacon/impressionrequest 212 of FIG. 2) shows that the correct demographics indicate thatthe actual person corresponding to the impression is a female (F) of age29. Example Equations 1 and 2 above may be used to correct uniqueaudience sizes and total impression counts that are affected bymisattribution errors such as the misattribution error 308 of FIG. 3.

FIG. 4 illustrates an example table 400 with example audience adjustment(AA) factors 402 (e.g., the audience adjustment factor (f_(i,j)) ofEquation 1 above) for unique audience sizes of different demographic(DEMO) groups. Based on the AME impressions 302 of FIG. 3, the audienceadjustment factor determiner 232 of FIG. 2 can use Equation 1 above tocalculate the example audience adjustment factors 402 for differentdemographic groups (j) that access particular media (i). The exampletable 400 shows AME UA sizes 404 and example DP UA sizes 406 fordifferent demographic groups. The example AME UA sizes 404 correspond tothe term (T_(i,j)) of Equation 1 above, and the DP UA sizes 406correspond to the term (F_(i,j)) of Equation 1 above. In the illustratedexample of FIG. 4, the AME UA sizes 404 show that the AME impressions302 of FIG. 3 include three (3) unique audience members of the F<50 demogroup, one (1) unique audience member of the F>=50 demo group, two (2)unique audience members of the M<50 demo group, and one (1) uniqueaudience member of the M>=50 demo group. The example DP UA sizes 406show that the DP impressions 304 of FIG. 3 include two (2) uniqueaudience members of the F<50 demo group, one (1) unique audience memberof the F>=50 demo group, two (2) unique audience members of the M<50demo group, and one (1) unique audience member of the M>=50 demo group.

The example DP UA sizes 406 have a misattribution-based error 410 forthe F<50 demo group which results from the misattribution error 308 ofFIG. 3. That is, the misattribution error 308 of FIG. 3 mistakenlyidentifies impression 9 (IMP #9) as corresponding to a male (M) of age30 rather than the correct demographic of female (F) of age 29, as notedin the AME impression 302. Because of the misattribution error 308 ofFIG. 3, impression 9 (IMP #9) for the DP impressions 304 is not countedfor a female (F) of age 29. Therefore, the DP UA 406 of FIG. 4 for theF<50 demo group is only two (2), which is less than the correct (truth)unique audience for the F<50 demo group of three (3) as shown by thecorresponding AME UA size 404. Because there were no othermisattribution errors in the example impressions of FIG. 3, the DP UAsizes 406 match corresponding AME UA sizes 404 for the other demogroups.

In the illustrated example, the audience adjustment factor determiner232 of FIG. 2 uses Equation 1 above to determine the AA factors 402. Forexample, for each of the demo groups F<50, F>=50, M<50, and M>=50, theaudience adjustment factor determiner 232 divides the corresponding AMEUA 404 (T_(i,j)) by the corresponding DP UA 406 (F_(i,j)) to determinethe corresponding AA factor 402 for that demo group. As shown in theexample table 400, the AA factor 402 corresponding to the DP UA 406having the misattribution-based error 410, the corresponding AA factor402 is 0.67 (e.g., T_(i,j)/F_(i,j)=>3/2=0.67).

FIG. 5 illustrates an example table 500 with example impressionadjustment (IA) factors 502 (e.g., the impressions adjustment factor(k_(i,j)) of Equation 2 above) for total AME impression counts 504 andtotal DP impression counts 506 of different demographic groups (j)determined based on the example AME impressions 302 and the example DPimpressions 304 of FIG. 3. In the illustrated example of FIG. 5, amisattribution-based error 508 occurs in association with the F<50demographic group, and a misattribution-based error 510 occurs inassociation with the M<50 demographic group. The misattribution-basederrors 508, 510 occur because of the misattribution error 308 of FIG. 3.That is, since the misattribution error 308 incorrectly indicates a male(M) of age 30 instead of the correct female (F) of age 29, the DPimpressions count 506 for the F<50 demographic group has one fewerimpression than the correct (truth) AME impressions count 504 for theF<50 demographic group. In addition, the DP impressions count 506 forthe M<50 demographic group has one more impression than the correct(truth) AME impressions count for the M<50 demographic group.

In the illustrated example, the impressions adjustment factor determiner234 of FIG. 2 uses Equation 2 above to determine the IA factors 502. Forexample, for each of the demo groups F<50, F>=50, M<50, and M>=50, theimpressions adjustment factor determiner 234 processes Equation 2 usingthe corresponding AME impressions 504 (Q_(i,j)) of FIG. 5, thecorresponding DP impressions 506 (R_(i,j)) of FIG. 5, and the total AMEimpressions count (S_(i)) summed across all demographic groups, todetermine the corresponding IA factor 502 for that demo group. Forexample, using Equation 2 above, the impressions adjustment factordeterminer 234 subtracts the DP impressions 506 (R_(i,j)) of aparticular demographic group from the AME impressions 504 (Q_(i,j)) ofthe same demographic group, and divides the resulting difference by thetotal AME impressions count (S_(i)) summed across all demographic groups(e.g., IA factor 502=((AME impressions 504 (Q_(i,j)))−(DP impressions506 (R_(i,j))))/(total AME impressions count (S_(i)))).

As shown in the example table 500, the IA factor 502 corresponding tothe F<50 demographic group having the misattribution-based error 508 is11.11%, and the IA factor 502 corresponding to the M<50 demographicgroup having the misattribution-based error 510 is −11.11%. In theillustrated example, the IA factors 502 are 0.0% for the demographicgroups not having misattribution-based errors. In the illustratedexample, the impressions adjustment factor determiner 234 determines themisattribution-based error 508 of 11.11% for the F<50 demographic groupbased on Equation 2 above by subtracting the DP impressions 506(R_(i,j)) of 3 for the F<50 demographic group shown in FIG. 5 from theAME impressions 504 (Q_(i,j)) of 4 for the F<50 demographic group shownin FIG. 5, and divides the resulting difference of one (1) by the totalAME impressions count (S_(i)) of nine (9). Also in the illustratedexample, the impressions adjustment factor determiner 234 determines themisattribution-based error 508 of −11.11% for the M<50 demographic groupbased on Equation 2 above by subtracting the DP impressions 506(R_(i,j)) of 4 for the M<50 demographic group shown in FIG. 5 from theAME impressions 504 (Q_(i,j)) of 3 for the M<50 demographic group shownin FIG. 5, and divides the resulting difference of negative one (−1) bythe total AME impressions count (S_(i)) of nine (9).

The IA factors 502 of the illustrated example are percentages of thetotal AME impressions count 504 summed across all demographic groups.Thus, the IA factor 502 of 11.11% corresponding to the F<50 demographicgroup means that 11.11% of 9 total AME impressions (S_(i)) (i.e., thesum of all of the AME impressions 504 logged across all of thedemographic groups shown in FIG. 5) need to be added to the DPimpressions count 506 for the F<50 demographic group. For example,11.11% of nine (9) total AME impressions is one (1), which can be addedto the three (3) DP impressions count 506 for the F<50 demographic groupto make the DP impressions count 506 equal to the AME impressions count504 for the F<50 demographic group. In addition, the IA factor 502 of−11.11% corresponding to the M<50 demographic group means that −11.11%of the nine (9) total AME impressions (i.e., the sum of all of the AMEimpressions 504 logged across all of the demographic groups shown inFIG. 5) need to be added (or 11.11% need to be subtracted) from the DPimpressions count 506 for the M<50 demographic group. For example,−11.11% of nine (9) total AME impressions is negative one (−1), whichcan be added to the four (4) DP impressions count 506 for the M<50demographic group to make the DP impressions count 506 equal to the AMEimpressions count 504 for the M<50 demographic group. Thus, the effectof the 11.11% IA factor 502 for the F<50 demographic group and the−11.11% IA factor 502 for the M<50 demographic group is that one (1) DPimpression 506 is shifted away from the M<50 demographic group to theF<50 demographic group. In this manner, the total DP impressions 506summed across all of the demographic groups remains the same afterapplying the IA factors 502.

FIG. 6 illustrates an example table 600 with misattribution-corrected UAsize values 602 and misattribution-corrected impression counts 604 basedon the AA factors 402 of FIG. 4 and the IA factors 502 of FIG. 5 fordifferent demographic groups. The data of example table 600 illustrateshow UA size values and impression counts received by the AME 114 in theaggregate (e.g., not individual impression records) from the databaseproprietor 116 can be adjusted to correct for misattribution-basederrors. The aggregate DP UA size values are shown in the example table600 as DP decision tree (DT)-corrected UA size values 606. The aggregateDP impression count values are shown in the example table 600 as DPDT-corrected impression counts 608. To generate the DP DT-corrected UAsize values 606 and the DP DT-corrected impression counts 608, thedatabase proprietor 116 performs a profile correction by applying a DTmodel on demographic data used to log impressions. That is, duringinitial registration with the database subscriber 116, some subscribersmay provide inaccurate demographic information and/or may omit certaindemographic information. To fill in some of the missing demographicinformation in subscriber accounts, the database proprietor 116processes the demographic data in subscriber accounts using a DT modelthat produces the most likely outcomes for the missing demographic data.Any suitable DT model can be used by the database proprietor 116 tocorrect profile data for subscribers of the database proprietor 116.

In the illustrated example of FIG. 6, the example unique audiencecorrector 236 of FIG. 2 applies the AA factors 402 to the DT-correctedUA size values 606 to determine the misattribution-corrected UA sizevalues 602. That is, the unique audience corrector 236 divides aDT-corrected UA size value 606 for a demographic group by acorresponding AA factor 402 for the same demographic group to calculatea corresponding misattribution-corrected UA size value 602 (e.g.,(misattribution-corrected UA size)=(DT-corrected UA size)/(AA factor)).For example, for the F<50 demographic group, the unique audiencecorrector 236 divides the DT-corrected UA size value 606 of 63,000 bythe corresponding AA factor 402 of 0.67 to calculate themisattribution-corrected UA size value 602 of 94,500 (e.g.,94,500=63,000/0.67). Thus, using the AA factors 402 in this manner tocalculate the misattribution-corrected UA size values 602 substantiallyreduces or eliminates the effects that misattributed impressions loggedby the database proprietor 116 have on the DT-corrected UA size values606.

In the illustrated example of FIG. 6, the example impressions corrector238 of FIG. 2 applies the IA factors 502 to the DT-corrected impressioncounts 608 to determine the misattribution-corrected impression counts604. That is, the example impressions corrector 238 increases aDT-corrected impression count 608 for a demographic group based on acorresponding IA factor 502 for the same demographic group to calculatea corresponding misattribution-corrected impressions count 604. Inparticular, the example impressions corrector 238 multiples an IA factor502 for a demographic group by the total DP DT-corrected impressionscount 612 summed across all of the demographic groups to determine anumber of adjustment impressions by which to adjust the DP DT-correctedimpressions count 608 for the same demographic group corresponding tothe selected IA factor 502 (e.g., (adjustment impressions)=(IAfactor)×(total cross-demographic DP DT-corrected impressions)). Theexample impressions corrector 238 then adds the calculated adjustmentimpressions to the corresponding DP DT-corrected impressions count 608for the same demographic group to determine a correspondingmisattribution-corrected impressions count 604 (e.g.,(misattribution-corrected impressions count)=(DP DT-correctedimpressions count 608)+(adjustment impressions)).

For example, to determine the misattribution-corrected impressions 604corresponding to the F<50 demographic group, the example impressionscorrector 238 of FIG. 2 multiples the IA factor 502 of 11.11% for theF<50 demographic group by the total DP DT-corrected impressions count612 of 710,000 to calculate the adjustment impressions of 78,888. Theexample impressions corrector 238 then adds the 78,888 adjustmentimpressions to the corresponding DP DT-corrected impressions count 608of 210,000 for the F<50 demographic group to calculate themisattribution-corrected impressions count 604 of 288,889 for the F<50demographic group.

To determine the misattribution-corrected impressions 604 for the M<50demographic group, the example impressions corrector 238 of FIG. 2multiples the IA factor 502 of −11.11% for the M<50 demographic group bythe total DP DT-corrected impressions count 612 of 710,000 to calculatethe adjustment impressions of −78,888. The example impressions corrector238 then adds the −78,888 adjustment impressions to (or subtracts 78,888from) the corresponding DP DT-corrected impressions count 608 of 165,000for the M<50 demographic group to calculate the misattribution-correctedimpressions count 604 of 86,111 for the M<50 demographic group.

An alternative technique to determine the misattribution-correctedunique audience sizes involves using impressions frequency values asdescribed in connection with FIG. 7. FIG. 7 illustrates an example table700 with misattribution-corrected unique audience values 702 andmisattribution-corrected impression counts 604 determined based on theIA factors 502 of FIG. 5 and impression frequencies 706 for differentdemographic groups. As used herein, impressions frequency is a number oftotal impressions (e.g., a DP DT-corrected impression count 608 of FIG.6) divided by a quantity of unique audience members (e.g., aDT-corrected UA size value 606 of FIG. 6) (e.g., frequency=impressionscount/UA). For example, for the F<50 demographic group, the databaseproprietor impressions frequency 706 is 3.33, which is calculated bydividing 210,000 DP DT-corrected impressions by 63,000 DP DT-correctedUA. In the illustrated example of FIG. 7, after the example impressionscorrector 238 determines the misattributions-corrected impressions 604based on the IA factors 502 as described above in connection with FIG.6, the example unique audience corrector 236 divides themisattribution-corrected impressions 604 of 288,889 for the F<50demographic group by the DP frequency of 3.33 (for the F<50 demographicgroup) to calculate a misattribution-corrected UA size 702 of 86,667.The frequency-based approach to determining misattribution-correctedimpressions 704 preserves the impressions frequencies for thedemographic groups.

As shown in FIGS. 6 and 7, the misattribution-corrected UA sizes 602 ofFIG. 6 are different from the misattribution-corrected UA sizes 702 ofFIG. 7. In determining whether to use the AA factor approach describedabove in connection with FIG. 6 or the impressions frequency approachdescribed in connection with FIG. 7 to determinemisattribution-corrected UA sizes, both approaches can be applied overmultiple iterations on test data for which true UA sizes are known. Theapproach that produces the most accurate misattribution-corrected UAsizes relative to the true UA sizes can then be selected for use on realimpression data. Alternatively, the impression frequency approach may beselected if a party wishes to preserve impression frequency even if theaccuracies of resulting misattribution-corrected UA sizes are notoptimal.

An example advantage of example misattribution adjustment techniquesdisclosed herein is that the total DP DT-corrected impressions count 612(e.g., 710,000 impressions in FIGS. 6 and 7) remains the same aftercorrecting the data for misattribution errors. That is, impressions arenot changed, but are instead redistributed. For example, as shown inFIGS. 6 and 7, a total misattribution-corrected impressions count 614across all demographic groups is 710,000, which is equal to the DPDT-corrected impressions count 612 of 710,000.

FIG. 8 is a flow diagram representative of machine readable instructionsthat may be executed to implement the misattribution corrector 202 ofFIG. 2 to determine the AA factors 402 of FIGS. 4 and 6, the IA factors502 of FIGS. 5, 6, and 7, the misattribution-corrected unique audiencesizes 602 of FIG. 6, the misattribution-corrected unique audience sizes702 of FIG. 7, and the misattribution-corrected impression counts 604 ofFIGS. 6 and 7. In this example, the machine readable instructionscomprise one or more programs for execution by a processor such as theprocessor 912 shown in the example processor platform 900 discussedbelow in connection with FIG. 9. The program(s) may be embodied insoftware stored on a tangible computer readable storage medium such as aCD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), aBlu-ray disk, or a memory associated with the processor 912, but theentire program and/or parts thereof could alternatively be executed by adevice other than the processor 912 and/or embodied in firmware ordedicated hardware. Further, although the example program(s) is/aredescribed with reference to the flowchart illustrated in FIG. 8, manyother methods of implementing the example misattribution corrector 202may alternatively be used. For example, the order of execution of theblocks may be changed, and/or some of the blocks described may bechanged, eliminated, or combined.

As mentioned above, the example process(es) of FIG. 8 may be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a tangible computer readable storage medium suchas a hard disk drive, a flash memory, a read-only memory (ROM), acompact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example process(es) of FIG. 8 may be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

The example flow diagram of FIG. 8 is shown as two phases including anexample adjustment factors development phase 802 and an examplemisattribution correction phase 804. During the adjustment factorsdevelopment phase 802, the misattribution corrector 202 (FIG. 2)determines the AA factors 402 (FIGS. 4 and 6) and the IA factors 502(FIGS. 5 and 6) for different demographic groups based on development ortest impressions such as the impressions shown in table 300 of FIG. 3.During the misattribution correction phase 804, the misattributioncorrector 202 corrects aggregate impression data (e.g., unique audiencemeasures and total impression counts) generated based on impressionscollected by the database proprietor 116 (and/or one or more otherdatabase proprietors). For example, the misattribution corrector 202uses the AA factors 402 and/or the IA factors 502 to determine themisattribution corrected UA size values 602 of FIG. 6, themisattribution-corrected UA size values 702 of FIG. 7, and/or themisattribution-corrected impression counts 604 of FIGS. 6 and 7 fordifferent demographic groups. In some examples, the misattributioncorrection phase 804 may begin immediately after the adjustment factorsdevelopment phase 802. In other examples, the misattribution correctionphase 804 may begin after a significant amount of time (e.g., hours,days, weeks, etc.) has passed following the completion of the adjustmentfactors development phase 802. In some examples, the adjustment factorsdevelopment phase 802 and the misattribution correction phase 804 may beimplemented as part of a same program. In other examples, the adjustmentfactors development phase 802 and the misattribution correction phase804 may be implemented as two separate programs.

The example adjustment factors development phase 802 of FIG. 8 begins atblock 806 at which the AME impressions collector 218 collectsimpressions from the client device 102. For example, the AME impressionscollector 218 collects impressions using the techniques described abovein connection with FIG. 2. The DP impressions collector 230 obtainsdevelopment impression records from the database proprietor 116 thatcorrespond to AME panelists that are also subscribers of the databaseproprietor 116 (block 808). The misattribution corrector 202 selects ademographic group (block 810). For example, the misattribution corrector202 selects one of the demographic groups of FIGS. 4-7. The exampleimpressions adjustment factor determiner 234 (FIG. 2) determines an IAfactor 502 for the selected demographic group (block 812). For example,the impressions adjustment factor determiner 234 determines the IAfactor 502 using Equation 2 above and/or the technique described abovein connection with FIG. 5.

The example unique audience adjustment factor determiner 232 (FIG. 2)determines an AA factor 402 for the selected demographic group (block814). For example, the unique audience adjustment factor determiner 232determines the AA factor 402 using Equation 1 above and/or the techniquedescribed above in connection with FIG. 4. The misattribution corrector202 determines whether there is another demographic group for which todetermine adjustment factors (block 816). If there is anotherdemographic group, control returns to block 810. If there is not anotherdemographic group, the adjustment factors development phase 802 ends. Inthe illustrated example, after the adjustment factors development phase802 ends, the misattribution correction phase 804 begins based on the IAfactors 502 and the AA factors 402 determined during the adjustmentfactors development phase 802. In some examples, the adjustment factorsdevelopment phase 802 is repeated from time to time (e.g., after anumber of days, weeks, months, etc.) to update the IA factors 502 and/orthe AA factors 402. For example, the ability of the database proprietor116 to identify subscribers may change (e.g., increased or decreasedaccuracy) from time to time. As such, to increase the likelihood thatthe IA factors 502 and the AA factors 402 reflect such changes, theadjustment factors development phase 802 can be repeated from time totime.

In the misattribution correction phase 804, the DP impressions collector230 obtains the DP DT-corrected unique audience sizes 606 (FIG. 6) andDP DT-corrected impression counts 608 (FIG. 6) from the databaseproprietor 116 (block 818). The misattribution corrector 202 selects ademographic group (block 820). For example, the misattribution corrector202 selects one of the demographic groups of FIGS. 4-7. The exampleimpressions corrector 238 (FIG. 2) determines a misattribution-correctedimpressions count 604 (FIGS. 6 and 7) based on the IA factor 502 for theselected demographic group (block 822). For example, the impressionscorrector 238 can determine the misattribution-corrected impressionscount 604 as described above in connection with FIG. 6.

The misattribution corrector 202 determines whether to use impressionsfrequency to determine a misattribution-corrected unique audience size(block 824). For example, the misattribution corrector 202 may check aconfiguration setting in a file, a program, and/or a hardware settingindicating whether to determine a misattribution-corrected uniqueaudience size based on an impressions frequency 706 (FIG. 7). If themisattribution corrector 202 determines that it should determine amisattribution-corrected unique audience size based on an impressionsfrequency 706, the misattribution corrector 202 determines animpressions frequency 706 for the selected demographic profile (block826). For example, the misattribution corrector 202 may determine theimpressions frequency 706 for the selected demographic profile asdescribed above in connection with FIG. 7. If the misattributioncorrector 202 determines that it should not use an impressions frequency706 to determine a misattribution-corrected unique audience size,control advances to block 828 without determining an impressionsfrequency 706. In some examples, the impressions frequency 706 isdetermined by the database proprietor 116 and provided by the databaseproprietor 116 to the misattribution corrector 202 via the DPimpressions collector 230. In such examples, the misattributioncorrector 202 does not need to determine the impressions frequency 706.

At block 828, the example unique audience corrector 236 (FIG. 2)determines the misattribution-corrected UA size for the selecteddemographic group (block 828). For example, if the misattributioncorrector 202 determined at block 824 that the impressions frequency 706is to be used to determine the misattribution-corrected UA size 702(FIG. 7) for the selected demographic group, the unique audiencecorrector 236 determines the misattribution-corrected UA size 702 basedon the impressions frequency 706 of block 826 as described above inconnection with FIG. 7. Alternatively at block 828, if themisattribution corrector 202 determined at block 824 that theimpressions frequency 706 is not to be used to determine themisattribution-corrected UA size 602 (FIG. 6) for the selecteddemographic group, the unique audience corrector 236 determines themisattribution-corrected UA size 602 based on the AA factor 402 (FIG. 4)of the selected demographic group as described above in connection withFIG. 6.

The misattribution corrector 202 then determines whether there isanother demographic group for which misattribution-adjusted impressioncounts or misattribution-adjusted UA sizes are to be determined (block830). If there is another demographic group, control returns to block820. Otherwise, the example program of FIG. 8 ends.

FIG. 9 is a block diagram of an example processor platform 900 capableof executing the instructions of FIG. 9 to implement the misattributioncorrector 202 of FIG. 2. The processor platform 900 can be, for example,a server, a personal computer, or any other type of computing device.

The processor platform 900 of the illustrated example includes aprocessor 912. The processor 912 of the illustrated example is hardware.For example, the processor 912 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer.

In the illustrated example, the processor 912 implements the examplemisattribution corrector 202, the example AME impressions collector 218,the example DP impressions collector 230, the example audienceadjustment factor determiner 232, the example impressions adjustmentfactor determiner 234, the example unique audience corrector 236, and/orthe example impressions corrector 238 described above in connection withFIG. 2.

The processor 912 of the illustrated example includes a local memory 913(e.g., a cache). The processor 912 of the illustrated example is incommunication with a main memory including a volatile memory 914 and anon-volatile memory 916 via a bus 918. The volatile memory 914 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 816 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 914, 916 is controlledby a memory controller.

In the illustrated example, the local memory 913 stores the examplemodified ID map 228 described above in connection with FIG. 2. In otherexamples any one or more of the local memory 913, the random accessmemory 914, the read only memory 916, and/or a mass storage device 928may store the example modified ID map 228.

The processor platform 900 of the illustrated example also includes aninterface circuit 920. The interface circuit 920 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 922 are connectedto the interface circuit 920. The input device(s) 922 permit(s) a userto enter data and commands into the processor 912. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 924 are also connected to the interfacecircuit 920 of the illustrated example. The output devices 924 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a light emitting diode (LED), a printer and/or speakers).The interface circuit 920 of the illustrated example, thus, typicallyincludes a graphics driver card, a graphics driver chip or a graphicsdriver processor.

The interface circuit 920 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network926 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 900 of the illustrated example also includes oneor more mass storage devices 928 for storing software and/or data.Examples of such mass storage devices 928 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

Coded instructions 932 include the machine readable instructions of FIG.8 and may be stored in the mass storage device 928, in the volatilememory 914, in the non-volatile memory 916, and/or on a removabletangible computer readable storage medium such as a CD or DVD.

From the foregoing, it will be appreciate that methods, apparatus andarticles of manufacture have been disclosed which enhance the operationsof a computer to improve the accuracy of impression-based data such asunique audience and impression counts so that computers and processingsystems therein can be relied upon to produce audience analysisinformation with higher accuracies. In some examples, computeroperations can be made more efficient based on the above equations andtechniques for determining IA factors, AA factors,misattribution-corrected unique audience sizes, andmisattribution-corrected impression counts. That is, through the use ofthese processes, computers can operate more efficiently by relativelyquickly determining parameters and applying those parameters through theabove disclosed techniques to determine the misattribution-correcteddata. For example, using example processes disclosed herein, a computercan more efficiently and effectively identify misattribution errors(e.g., the misattribution error 308 of FIG. 3) in development or testdata logged by the AME 114 and the database proprietor 116 without usinglarge amounts of network communication bandwidth (e.g., conservingnetwork communication bandwidth) and without using large amounts ofcomputer processing resources (e.g., conserving processing resources) tocommunicate with individual online users to request survey responsesabout their online media access habits and without needing to rely onsuch survey responses from such online users. Survey responses fromonline users can be inaccurate due to inabilities or unwillingness ofusers to recollect online media accesses. Survey responses can also beincomplete, which could require additional processor resources toidentify and supplement incomplete survey responses. As such, examplesdisclosed herein more efficiently and effectively determinemisattribution-corrected data. Such misattribution-corrected data isuseful in subsequent processing for identifying exposure performances ofdifferent media so that media providers, advertisers, productmanufacturers, and/or service providers can make more informed decisionson how to spend advertising dollars and/or media production anddistribution dollars.

Furthermore, example methods, apparatus, and/or articles of manufacturedisclosed herein identify and overcome inaccuracies in impressionsand/or aggregate impression-based data provided by database proprietors.For example, example methods, apparatus, and/or articles of manufacturedisclosed herein overcome the technical problem of counting impressionsand determining unique audiences of media on media devices that areshared by multiple people. Example methods, apparatus, and/or articlesof manufacture disclosed herein solve this problem without forcing suchmedia devices to be used by only a single person and without forcingpeople to always login to their subscriber accounts of databaseproprietors. By not forcing logins into database proprietor accounts,examples disclosed herein do not force additional network communicationsto be employed, thus, reducing network traffic.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus, comprising: an impression collectorto: receive, at a first internet domain, a first request from a clientdevice, the first request indicative of access to media at the clientdevice; send, from the first internet domain, a response to the clientdevice, the response to instruct the client device to send a secondrequest to a second internet domain, the second request to be indicativeof the access to the media at the client device; an audience adjustmentfactor determiner to determine an audience adjustment factor for ademographic group based on first impressions reported by the clientdevice to the first internet domain and second impressions reported bythe client device to the second internet domain, the first and secondimpressions corresponding to the same media accessed on the clientdevice; and a unique audience corrector to determine amisattribution-corrected unique audience size for the demographic groupbased on the audience adjustment factor and based on a second uniqueaudience size determined at the second internet domain for thedemographic group, the second unique audience size having an error basedon third impressions misattributed at the second internet domain to thedemographic group when the third impressions correspond to anotherdemographic group.
 2. An apparatus as defined in claim 1, wherein theaudience adjustment factor determiner is to determine the audienceadjustment factor by dividing a third unique audience size correspondingto the first impressions by a fourth unique audience size correspondingto the second impressions.
 3. An apparatus as defined in claim 1,wherein the unique audience corrector is to determine themisattribution-corrected unique audience size for the demographic groupby dividing the second unique audience size by the audience adjustmentfactor.
 4. An apparatus as defined in claim 1, wherein the firstimpressions are reported by the client device to an audience measuremententity at the first internet domain that does not provide the media tothe client device, and a user of the client device is a panel member ofthe audience measurement entity.
 5. An apparatus as defined in claim 1,wherein the second impressions are reported by the client device to asocial network service at the second internet domain to which a user ofthe client device is subscribed.
 6. An apparatus as defined in claim 1,wherein the audience adjustment factor is to correct unique audiencesize values having inaccuracies due to impressions incorrectlyattributed to demographic data not corresponding to personscorresponding to the impressions.
 7. An apparatus as defined in claim 1,wherein the error in the second unique audience size is based on anentity at the second internet domain incorrectly identifying a user ofthe client device as belonging to the demographic group when the userbelongs to the another demographic group, the misattribution-correctedunique audience size comprising dividing the second unique audience sizeby the audience adjustment factor.
 8. An apparatus as defined in claim1, wherein the unique audience corrector is to determine themisattribution-corrected unique audience size based on the audienceadjustment factor without communicating with individual online usersabout their online media access habits and without using surveyresponses from the online users to determine the error.
 9. An apparatusas defined in claim 8, wherein by determining themisattribution-corrected unique audience size, the unique audiencecorrector conserves network communication bandwidth by not communicatingwith individual online users about their online media access habits andby not requesting survey responses from the online users to determinethe error.
 10. An apparatus as defined in claim 8, wherein bydetermining the misattribution-corrected unique audience size, theunique audience corrector conserves computer processing resources by notcommunicating with individual online users about their online mediaaccess habits and by not requesting survey responses from the onlineusers to determine the error.
 11. A non-transitory computer readablemedium comprising instructions that, when executed, cause a machine toat least: receive, at a first internet domain, a first request from aclient device, the first request indicative of access to media at theclient device; send, from the first internet domain, a response to theclient device, the response to instruct the client device to send asecond request to a second internet domain, the second request to beindicative of the access to the media at the client device; determine anaudience adjustment factor for a demographic group based on firstimpressions reported by the client device to the first internet domainand second impressions reported by the client device to the secondinternet domain, the first and second impressions corresponding to thesame media accessed on the client device; and determine amisattribution-corrected unique audience size for the demographic groupbased on the audience adjustment factor and based on a second uniqueaudience size determined at the second internet domain for thedemographic group, the second unique audience size having an error basedon third impressions misattributed at the second internet domain to thedemographic group when the third impressions correspond to anotherdemographic group.
 12. A non-transitory computer readable medium asdefined in claim 11, wherein the instructions are further to cause themachine to determine the audience adjustment factor by dividing a thirdunique audience size corresponding to the first impressions by a fourthunique audience size corresponding to the second impressions.
 13. Anon-transitory computer readable medium as defined in claim 11, whereinthe instructions are further to cause the machine to determine themisattribution-corrected unique audience size for the demographic groupby dividing the second unique audience size by the audience adjustmentfactor.
 14. A non-transitory computer readable medium as defined inclaim 11, wherein the first impressions are reported by the clientdevice to an audience measurement entity at the first internet domainthat does not provide the media to the client device, and a user of theclient device is a panel member of the audience measurement entity. 15.A non-transitory computer readable medium as defined in claim 11,wherein the second impressions are reported by the client device to asocial network service at the second internet domain to which a user ofthe client device is subscribed.
 16. A non-transitory computer readablemedium as defined in claim 11, wherein the audience adjustment factor isto correct unique audience size values having inaccuracies due toimpressions incorrectly attributed to demographic data not correspondingto persons corresponding to the impressions.
 17. A non-transitorycomputer readable medium as defined in claim 11, wherein the error inthe second unique audience size is based on an entity at the secondinternet domain incorrectly identifying a user of the client device asbelonging to the demographic group when the user belongs to the anotherdemographic group, the misattribution-corrected unique audience sizebeing different than the second unique audience size based on dividingthe second unique audience size by the audience adjustment factor.
 18. Anon-transitory computer readable medium as defined in claim 11, whereinthe instructions further cause the machine to determine themisattribution-corrected unique audience size based on the audienceadjustment factor without communicating with individual online usersabout their online media access habits and without using surveyresponses from the online users to determine the error.
 19. Anon-transitory computer readable medium as defined in claim 18, whereinthe instructions further cause the machine to conserve networkcommunication bandwidth by not communicating with individual onlineusers about their online media access habits and by not requestingsurvey responses from the online users to determine the error.
 20. Anon-transitory computer readable medium as defined in claim 18, whereinthe instructions further cause the machine to conserve computerprocessing resources by not communicating with individual online usersabout their online media access habits and by not requesting surveyresponses from the online users to determine the error.